{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "initial_id",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-24T06:01:03.618098Z",
     "start_time": "2024-11-24T06:00:47.464994Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.4.0/pyLDAvis/js/ldavis.v1.0.0.css\">\n",
       "\n",
       "\n",
       "<div id=\"ldavis_el768422558741625128815516335\" style=\"background-color:white;\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
       "var ldavis_el768422558741625128815516335_data = {\"mdsDat\": {\"x\": [-0.0793970799777082, 0.004256153907084002, 0.043813213447244585, -0.0826535818714631, -0.1954850361958386, 0.0023542900832903366, 0.2362866748436124, -0.27684232229923833, 0.1787005106089606, 0.1694208824768025, 0.003147966614635793, -0.14757364017298855, 0.14397196853560668], \"y\": [0.1423472595292065, -0.2392070951184603, 0.11036112572681521, -0.0031514155331781955, 0.10380588166086162, 0.27379167485861416, 0.02869332192116028, -0.04689934011906915, -0.19392453033836576, 0.14497656629428826, -0.08100872138876869, -0.16356784339847596, -0.0762168840946279], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13], \"cluster\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], \"Freq\": [11.035271770457966, 10.395482762957563, 10.264243128623326, 9.224200799047594, 9.203098148662678, 7.635769951155308, 7.103281745710424, 6.9738839180363, 6.807692655949628, 6.22450643606541, 5.6589761293430945, 5.3167703976060325, 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       "\n",
       "function LDAvis_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(LDAvis) !== \"undefined\"){\n",
       "   // already loaded: just create the visualization\n",
       "   !function(LDAvis){\n",
       "       new LDAvis(\"#\" + \"ldavis_el768422558741625128815516335\", ldavis_el768422558741625128815516335_data);\n",
       "   }(LDAvis);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/LDAvis\n",
       "   require.config({paths: {d3: \"https://d3js.org/d3.v5\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.4.0/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
       "        new LDAvis(\"#\" + \"ldavis_el768422558741625128815516335\", ldavis_el768422558741625128815516335_data);\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & LDAvis\n",
       "    LDAvis_load_lib(\"https://d3js.org/d3.v5.js\", function(){\n",
       "         LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.4.0/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
       "                 new LDAvis(\"#\" + \"ldavis_el768422558741625128815516335\", ldavis_el768422558741625128815516335_data);\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import codecs\n",
    "\n",
    "import os\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "from gensim import corpora\n",
    "from gensim.models import LdaModel\n",
    "from gensim.corpora import Dictionary\n",
    "\n",
    "import pyLDAvis.gensim\n",
    "import pyLDAvis\n",
    "import pyLDAvis.gensim_models as gensimvis\n",
    "from gensim.models import LdaModel\n",
    "from gensim.corpora import Dictionary\n",
    "\n",
    "train = []\n",
    "\n",
    "fp = codecs.open('D:\\\\pycharmproject\\\\高校图书采购主题分析\\\\CNKI-output.txt','r',encoding='utf8')\n",
    "for line in fp:\n",
    "    if line != '':\n",
    "        line = line.split()\n",
    "        train.append([w for w in line])\n",
    "\n",
    "dictionary = corpora.Dictionary(train)\n",
    "\n",
    "corpus = [dictionary.doc2bow(text) for text in train]\n",
    "\n",
    "lda = LdaModel(corpus=corpus, id2word=dictionary, num_topics=13, passes=30)\n",
    "# num_topics：主题数目\n",
    "# passes：训练伦次\n",
    "# num_words：每个主题下输出的term的数目\n",
    "'''\n",
    "for topic in lda.print_topics(num_words = 20):\n",
    "    termNumber = topic[0]\n",
    "    print(topic[0], ':', sep='')\n",
    "    listOfTerms = topic[1].split('+')\n",
    "    for term in listOfTerms:\n",
    "        listItems = term.split('*')\n",
    "        print('  ', listItems[1], '(', listItems[0], ')', sep='')\n",
    "\n",
    "print(type(vis_data))\n",
    "print(type(lda))\n",
    "print(type(corpus))\n",
    "print(type(dictionary))\n",
    "'''\n",
    "\n",
    "# 准备可视化数据 换成 mmds 效果更好\n",
    "vis_data = pyLDAvis.gensim.prepare(lda, corpus, dictionary, mds='mmds')\n",
    "\n",
    "#直接显示\n",
    "pyLDAvis.display(vis_data)\n",
    "\n",
    "\n",
    "# 保存为 HTML 文件\n",
    "#pyLDAvis.save_html(vis_data, 'E:\\\\allme\\\\learning\\\\reproducing-the-code-from-a-paper\\\\rcp0810-for-CNIK-Data\\\\ldavis.html')\n",
    "# 打印当前工作目录 C:\\Users\\Evie\n",
    "#print(os.getcwd())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d83ed66-875a-4060-ba32-678203621066",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "language": "python",
   "name": "python3"
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    "name": "ipython",
    "version": 3
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
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